The thing is that rather than just go with the input representation (say N descriptors) a deep learning system is supposed to a learn an abstract form of the representation. In some ways it’s like PCA or MDS.

Agreed about the new graph descriptor – not sure whether it was necessary for their modeling method; or they just decided to add one more descriptor to the literature

On your last point, my understanding is that deep learning is (in general) independent of the modeling method that uses the learned representation. This doesn’t avoid the fact that some modeling methods might do better than others

]]>By: Egon Willighagenhttp://blog.rguha.net/?p=1112&cpage=1#comment-416630
Egon WillighagenFri, 05 Jul 2013 06:09:25 +0000http://blog.rguha.net/?p=1112#comment-416630I do not know “deep learning” much. But it sounds to me that it basically addresses the point that the representation of your system determines how much detail your model can learn. That is nothing new.

So, from your introduction I understand that deep learning learns a new representation? But the Lusci did not have the system learn a new representation, they just selected a new representation?

Now, moving from a graph to a graph representation does not sound to be a significant difference, so that lack of improvement in prediction performance does not sounds unexpected. For example, a DAG needs to be adapted to support delocalization just as well.

But, what I am wondering more is my observation that you cannot split out representation from modeling method. That is, the representation determines how easily the modeling method can learn the patterns. Not the information content, but how the information is represented too.

]]>By: Tobiashttp://blog.rguha.net/?p=1112&cpage=1#comment-416541
TobiasWed, 03 Jul 2013 07:07:23 +0000http://blog.rguha.net/?p=1112#comment-416541HI Rajarshi,
thanks for the write up, always interesting to learn something new and get some new perspectives and pointers to data and publications.

I actually liked that Lusci, Pollastri and Baldi provided their data to reproduce their findings and also their source code of UG-RNN at AquaSol http://cdb.ics.uci.edu/

Looking at the source code (Python and C++) it becomes quite clear that for me as a practitioner it would be hard or impossible to implement that from scratch, but I can use, change or apply it.

With the vague description from the Merck-Kaggle team I cant do anything, well besides learning everything by myself, which I wont do (so little time, so much to do).

Thanks for the link to the Burnham data, which let me stumble upon your paper (10.1016/j.bmc.2011.05.005). I think its incredible how much data is available now, compared to a couple of years ago.